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Speaker 1: The brain is massively complex, so how have people tried

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Speaker 1: to crack its mysteries? And what does this always have

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Speaker 1: to do with the latest technologies that are available, And

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Speaker 1: what does the history of neuroscience have to do with

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Speaker 1: feeling bumps on someone's skull? Electricity, Frankenstein, animatronics, telegraphs, telephone exchanges, computers, LMS,

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Speaker 1: and the next metaphor that we're going to use to

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Speaker 1: try to capture the brain's magic. Welcome to Inner Cosmos

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Speaker 1: with me David Eagelman. I'm a neuroscientist and an author

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Speaker 1: at Stanford, and in these episodes we seek to understand

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Speaker 1: why and how our lives look the way they do.

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Speaker 1: If you reach out and you wrap your hand around

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Speaker 1: your coffee cup and then bring it up to your lips,

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Speaker 1: that all seems pretty effortless. But what's driving that motor

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Speaker 1: action is a massive, invisible puppeteer living inside your skull,

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Speaker 1: an organ made of tens of billions of very active

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Speaker 1: cells called neurons, each one of which is firing off

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Speaker 1: tiny electrical pulses tens or hundreds of times every second,

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Speaker 1: and each of which is exchanging messages with thousands of

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Speaker 1: other neurons. And somehow from this vast electrical storm, you

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Speaker 1: get your hand moving, and you get your perception of

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Speaker 1: the tattooed barista in a rolled up flannel pulling espresso

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Speaker 1: shots and their of students hunched over laptops, and the

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Speaker 1: scent of dark roast and cinnamon. And you get your

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Speaker 1: memory of your name and your first kiss and what

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Speaker 1: you had for breakfast. And generally, from this lightning storm

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Speaker 1: of activity in the brain emerges the shimmering.

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Speaker 2: Something that we call you.

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Speaker 1: Now, we humans have been trying to understand this wrinkled

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Speaker 1: three pounds of jelly for a long time, because it's

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Speaker 1: been obvious that this is where all the action is.

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Speaker 1: Why is it obvious that is all happening in the brain. Well,

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Speaker 1: if you lose your leg, or your kidney or an eyeball,

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Speaker 1: those are all terrible events, but you.

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Speaker 2: Are still the same. But if you damage even a

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Speaker 2: very small chunk of brain tissue, that can change you entirely.

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Speaker 1: It changes your personality, or your decision making or your thoughts.

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Speaker 1: It changes who you are. So that led people to

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Speaker 1: realize slowly that somehow all the action of your behaviors

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Speaker 1: and your conscious experience is all tied to these three

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Speaker 1: pounds of tissue. So that sounds good, but it's really

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Speaker 1: hard to crack the mystery of how this thing works.

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Speaker 1: And that's for two main reasons. First, all the action

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Speaker 1: going on is microscopic. When you look at it, you

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Speaker 1: can't see anything. It just looks like a big, wrinkly

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Speaker 1: thing with the consistency of mashed potatoes. You'll often hear

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Speaker 1: me and others say that this is the most complex

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Speaker 1: thing we've ever discovered on our planet, but that is

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Speaker 1: a modern revelation. There's nothing obvious about that statement when

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Speaker 1: you look at the brain, and in fact, ancient cultures

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Speaker 1: used to throw out the brain at autopsy and be

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Speaker 1: much more interested in other organs. So the brain's doing

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Speaker 1: all kinds of things, but you can't see that. The

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Speaker 1: second challenge is that the brain is locked up tightly

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Speaker 1: inside the skull. It makes sense that it's well protected

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Speaker 1: as very delicate stuff, but that has made it especially

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Speaker 1: hard to study. So what's happened is that people have

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Speaker 1: been trying to decipher this very complicated puzzle for a

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Speaker 1: long time, and the history of neuroscience is, in a

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Speaker 1: way the history of human self regard. Each generation peers

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Speaker 1: into the darkness and sees its own technology reflected back.

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Speaker 1: So in the seventeenth century, when hydraulics were the cutting

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Speaker 1: edge of technology, the brain was imagined as a network

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Speaker 1: of pipes and valves.

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Speaker 2: Thoughts and sensations flowed like.

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Speaker 1: Water, propelled by animal spirits that coursed through hollow nerves.

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Speaker 1: Then in the eighteenth century, as electricity captured the imagination,

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Speaker 1: the brain became a battery. It became a generator of sparks.

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Speaker 1: By the nineteenth century it was a telegraph system, with

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Speaker 1: messages darting along wires and transmitting coded signals from one

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Speaker 1: station to another.

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Speaker 2: And then as we moved into.

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Speaker 1: The twentieth century with advancing technology, the metaphor shifted again.

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Speaker 2: The brain became a telephone.

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Speaker 1: Exchange, and then an electronic circuit and eventually a digital computer.

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Speaker 1: Now the cool thing is that each metaphor brought new insights.

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Speaker 1: So hydraulics inspired experiments on fluid pressure in the brain,

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Speaker 1: and telegraphs shaped the search for nerve conduction, and the

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Speaker 1: computer age fueled the rise.

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Speaker 2: Of neural network models.

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Speaker 1: But each metaphor also carries its blind spots. We become

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Speaker 1: experts at seeing the brain through the lens of the

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Speaker 1: day's technology, and sometimes we mistake the map for the territory.

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Speaker 1: So nowadays we've got great tools. We have fMRI scanners

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Speaker 1: that can track activity millimeter by millimeter. We have optogenetics

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Speaker 1: that can tickle neurons on or off with light. We

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Speaker 1: have genetic tools to clip out sections or even single letters.

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Speaker 1: And yet the deepest mysteries of the brain still remain,

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Speaker 1: and they're still stubborn. How does a three pound lump

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Speaker 1: of biological tissue generate the first person experience of being alive?

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Speaker 1: How does matter become mind? And how do all the

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Speaker 1: technology metaphors that we use today blind us to some

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Speaker 1: of the details. So that's why today I called up

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Speaker 1: Matthew Cobb. He's an evolutionary neurobiologist who studies smell and

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Speaker 1: memory at the University of Manchester, and he's also a

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Speaker 1: historian who has spent decades tracing the shifting stories that

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Speaker 1: we tell about the brain. He wrote a great book

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Speaker 1: called The Idea of the Brain, and this is a

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Speaker 1: fascinating history of neuroscience because it's not just a list

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Speaker 1: of events, but instead it explores how science and culture

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Speaker 1: and technology all braided together to shape our ideas and

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Speaker 1: how time and again we've been certain that we were

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Speaker 1: on the brink of a full explanation, only to discover

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Speaker 1: that the brain is more complex than we imagined. You

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Speaker 1: can't read this book without getting a deep appreciation for

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Speaker 1: the centuries of labor that.

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Speaker 2: It took to get us to the modern day picture.

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Speaker 1: The history of neuroscience is about hundreds or thousands of

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Speaker 1: people making small contributions, and presumably each person could never

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Speaker 1: know how seminole the contribution was. They were just providing

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Speaker 1: one puzzle piece that would end up clicking together with

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Speaker 1: other pieces in the future, but it's always impossible to see.

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Speaker 1: So the key is that in the end, the story

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Speaker 1: of the brain is also the story of us. It's

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Speaker 1: our technology, is what we understand at any point in history,

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Speaker 1: and of course our relentless drive to understand the organ

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Speaker 1: that is doing the understanding. So here's my conversation with

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Speaker 1: Matthew Cobb. Okay, so, Matthew, I'm a giant fan of

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Speaker 1: your book, The Idea of the Brain. I thought it

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Speaker 1: was extraordinary because it's not just a history of neuroscience

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Speaker 1: that is, it includes that, but much more importantly, it's

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Speaker 1: a history of how to think about the brain, how

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Speaker 1: people have thought about it.

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Speaker 2: And one of your main points in the book.

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Speaker 1: Is that we always draw on our technology as a metaphor.

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Speaker 1: So science is not just an accumulation of facts, but

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Speaker 1: instead we frame things in particular ways depending on what's

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Speaker 1: going on around us.

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Speaker 2: So let's start there this.

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Speaker 3: Issue that you've just described a metaphor, of the metaphors

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Speaker 3: that we use, and how that changes over time. That

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Speaker 3: actually gives a frame to the book and makes the history,

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Speaker 3: which otherwise might be a bit dull, actually much more

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Speaker 3: interesting and enables you to think about how people could

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Speaker 3: think the way they didn't, why they couldn't see anything different,

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Speaker 3: Because that's always the problem with history, right, I mean,

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Speaker 3: it looks so obvious to us, you know how why

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Speaker 3: on earth didn't people realize that the brain is the

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Speaker 3: center of thought?

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Speaker 4: Why do they think it was the heart? And one

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Speaker 4: of the rules.

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Speaker 3: I think about history, in particular the history of science,

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Speaker 3: is that you're not allowed to think that people in

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Speaker 3: the past were stupid because they were amazingly smart.

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Speaker 4: They just didn't know as much as we know. And

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Speaker 4: we only know.

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Speaker 3: That most of us because we've read it or we've

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Speaker 3: been taught it in class. Relatively few people have actually

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Speaker 3: made great breakthroughs that have changed how we think about

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Speaker 3: the world. So when you've got these people who are very,

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Speaker 3: very clever but can't see something, that the task is

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Speaker 3: to try and put yourselves in their heads and say, well,

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Speaker 3: what is it they don't understand And it can't just

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Speaker 3: be the answer, you know, they didn't understand that nothing

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Speaker 3: goes faster than light or whatever. You've got to think about, well,

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Speaker 3: what is it the framework that they're living in the

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Speaker 3: world they're living in. Trying and put yourselves in their shoes,

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Speaker 3: and then it all starts to become clearer. And it also,

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Speaker 3: I think for the reader, both the lay reader and

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Speaker 3: for scientists becomes much more interesting because then you can

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Speaker 3: start to say not only about now, but also about

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Speaker 3: the future, about what might be coming and why we

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Speaker 3: can't understand certain things at the moment.

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Speaker 4: You know.

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Speaker 1: One of the things that was quite stunning for me

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Speaker 1: is I, like, you, have spent my whole career in neuroscience,

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Speaker 1: but it was difficult for me to envision what it

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Speaker 1: would have been like to live in neuroscience in an

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Speaker 1: era where we didn't know something. So for example, the

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Speaker 1: discovery of the action potential. This is where a neuron

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Speaker 1: has a spike there, zips down the axon and carries

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Speaker 1: signals rapidly in this way. I grew up just taking

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Speaker 1: for granted that that's what neurons do.

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Speaker 4: That.

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Speaker 1: Of course, the brain uses electricity in this sense. But

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Speaker 1: you know, you had a whole chapter about electricity and

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Speaker 1: how people discovered that that was going on with the brain.

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Speaker 2: Give us a sense of that.

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Speaker 4: Well, it all began, I guess it began in America

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Speaker 4: really with.

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Speaker 3: The ability to Franklin to actually bring electricity down very

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Speaker 3: dangerous experiments as everybody knows, with kites and keys and

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Speaker 3: that kind of thing. But in the eighteenth century people

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Speaker 3: began to be able to store electricity. They could produce

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Speaker 3: it in the form of static electricity. So they'd get

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Speaker 3: some amber or any kind of resin and you know,

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Speaker 3: just like you, you know, maybe you do.

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Speaker 4: This for your kid.

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Speaker 3: So you know when you were a kid, your dad

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Speaker 3: would do this. You get a balloon, you rub it

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Speaker 3: on his jump and he stick it on the wall. Amazing, right,

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Speaker 3: that static electricity you can actually generate by putting say

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Speaker 3: some wool onto a resin wheel. That you spin round

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Speaker 3: and they would do These people would do these kind

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Speaker 3: of party tricks. They were called electricians, right, and they

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Speaker 3: would go into your fancy house and they would they

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Speaker 3: would do like one of these shows was called the

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Speaker 3: Feathered Boy, and they'd get some hapless child and winch

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Speaker 3: him up to the ceiling and then they'd charge him

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Speaker 3: up with electricity using this amber and cloth, and then

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Speaker 3: they'd throw a load of feathers in the air and

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Speaker 3: of course they'd all stick on it. So they could

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Speaker 3: do stuff like this. And also people were thinking about

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Speaker 3: actually using a very primitive form of electroshock therapy. So

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Speaker 3: these electricians, when you could generate it, they would travel

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Speaker 3: around the countryside, you know, itinerant electricians, so too particularly

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Speaker 3: significant figures. So John Wesley, the founder of Methodism, and Maha,

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Speaker 3: the French revolutionary, were both itinerant electricians in the UK

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Speaker 3: in the eighteenth century.

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Speaker 4: Wandering around. Somebody say I'm.

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Speaker 3: Feeling really miserable and terrible, and he said, well, just

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Speaker 3: hold on to this, and then they'd wind it up

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Speaker 3: and bang, you get an electric shark. So scientists knew

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Speaker 3: that electricity was doing something, but that didn't mean to

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Speaker 3: say that it was how bodies worked, and it became

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Speaker 3: increasingly complex as people were able to actually store electricity

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Speaker 3: through something called a lidon jar. You'd actually put it

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Speaker 3: into a generate this electricity and it would then stay.

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Speaker 3: But what happened it was then suddenly discharge if you

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Speaker 3: touched it, like touching a cow fence.

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Speaker 4: Right, you know, you know you've been touched.

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Speaker 3: And so when French scientists didn't experiment with this, he

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Speaker 3: got four hundred monks, which is they're all holding hands,

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Speaker 3: and at one end he got one of these big

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Speaker 3: jars full of electricity and he made the monk touch it,

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Speaker 3: and then he watched us that they all jumped up

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Speaker 3: as if the chargers down this line of monks must

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Speaker 3: have been about whatever eight hundred meters more thousand meters

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Speaker 3: kilometer along a monk. And then finally they were able

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Speaker 3: to use They were showing that even they're doing experiments

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Speaker 3: on animals, like in particular frogs, and they'd noticed that

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Speaker 3: if you stimulated a frog nerve on attached to a leg,

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Speaker 3: then their muscle would contract. Now this didn't show you

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Speaker 3: what was going on, because you know if you put

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Speaker 3: acid on a nerve then that it will also contract

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Speaker 3: so maybe electricy was just an irritant, or maybe it

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Speaker 3: was actually in bodies right, And this was.

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Speaker 4: A big argument.

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Speaker 3: One of the things that people stuid was electric fish,

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Speaker 3: so the electric eel. And they found this structure that

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Speaker 3: was produced as shock and at the beginning of the

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Speaker 3: nineteenth century chap called vaulta hence vault. He decided he'd

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Speaker 3: do a bit of biomimicry. He said, okay, well they've

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Speaker 3: got this electric organ. Maybe I can use that electric organ.

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Speaker 3: I think I can mimic the structure of this which

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Speaker 3: had kind of layers and produce an electric current. And

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Speaker 3: so he did created what he called a pile, which

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Speaker 3: was layers of zinc and cardboard with los of acid

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Speaker 3: around them. And it was because it was a black

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Speaker 3: pile of pennies. And this would then produce electricity, but

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Speaker 3: at a constant rate.

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Speaker 4: Now in English we now call this a battery.

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Speaker 3: So you've now got this continuous release of electricity. And

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Speaker 3: then you can see, well, actually we can stimulate if

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Speaker 3: we put these electrodes onto and they did very horrible

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Speaker 3: experiments on animals, dead animals. You know, you could then

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Speaker 3: make it if you put electrodes on either side of

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Speaker 3: a cow's head, then its mouth would start to move,

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Speaker 3: and its eyes would roll in its sockets. And in

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Speaker 3: one particularly awful experiment in London, then a criminal who

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Speaker 3: killed his wife and child and had been hanged was

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Speaker 3: immediately taken down from the gallows as soon as he

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Speaker 3: was dead, taken into a small room with about twelve

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Speaker 3: learned gentleman and this experiment was done on his dead body,

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Speaker 3: and of course he would then, you know, his arms

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Speaker 3: started flailing about and all the rest of it.

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Speaker 4: That these experiments convinced.

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Speaker 3: People that there was electricity in nerves, that it wasn't

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Speaker 3: just an irritant, that it was actually some kind of

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Speaker 3: organizing principle.

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Speaker 1: And by the way, this is starting to be one

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Speaker 1: of the origins of Mary Shelley's frankenstn.

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Speaker 3: It's not known, but she did regularly go to something

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Speaker 3: called the Royal Institution in London, which is still there,

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Speaker 3: amazing lecture theater where science is communicated to the public,

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Speaker 3: and various British scientists spoke there, and at the beginning

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Speaker 3: in about eighteen fourteen, there were a series of demonstrations

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Speaker 3: of this power of electricity to animate dead bodies on

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Speaker 3: sheep's heads and so on. You can go to the

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Speaker 3: theater and see it. I mean, it was a huge,

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Speaker 3: huge thing. And we don't know that Mary Godwin went,

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Speaker 3: but it's pretty likely she did. And then a couple

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Speaker 3: of years later, when she was whatever sixteen, she ran

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Speaker 3: off with Shelley and they went on honeymoon. So they

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Speaker 3: sat around in Morote ghost stories and she wrote Frankistan.

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Speaker 1: One of the fascinations that I enjoyed with reading your

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Speaker 1: book was all these things that we learned from biology

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Speaker 1: that have created new technologies, like looking at the electric

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Speaker 1: eel and the invention of the battery that fade away.

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Speaker 1: Now we have a gajillion times more batteries on our

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Speaker 1: planet than we have electric eels, but we have forgotten

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Speaker 1: where that came from, that biomimicry. So what I really

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Speaker 1: loved is the way you tie these things together. So

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Speaker 1: tell us about phrenology, the pseudoscience of phrenology that was popular,

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Speaker 1: and how you think this actually contributed in a positive

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Speaker 1: way to how we think about neuroscience and modern times,

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Speaker 1: even though phrenology itself was incorrect.

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Speaker 3: If you want to have an argument with a neuroscientist,

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Speaker 3: your studies the human brain in particular go groups them together.

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Speaker 3: Say is function localized? Is there part of our brain

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Speaker 3: that is devoted to doing a particular thing and that

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Speaker 3: if you remove it you can't do that thing?

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Speaker 4: Right?

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Speaker 3: The answer is kind of yes, and no. Man is complicated, right.

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Speaker 3: But in the nineteenth century it tormented people a great deal,

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Speaker 3: partly because of the influence of French philosophy, and that

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Speaker 3: the identification of the brain and the mind caused a

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Speaker 3: huge problem because Descartes, who was for the French, the

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Speaker 3: philosopher and no need for any other. He insisted that

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Speaker 3: the mind was a united structure and therefore the brain

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Speaker 3: must be as well. And if you look at the brain,

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Speaker 3: it appears identical on either half, got two halves, and

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Speaker 3: they look pretty symmetrical. So the French were very convinced

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Speaker 3: that there was no localization of function. And yet there

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Speaker 3: was this popular idea of phrenology, which is, you know,

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Speaker 3: kind of grew up in the late eighteenth cent and

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Speaker 3: was basically a version of oh, I don't like the

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Speaker 3: look of his face, his eyes are too close together,

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Speaker 3: or whatever, you know, version you he's got big ears,

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Speaker 3: or whatever. Way of looking at somebody's face and not

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Speaker 3: liking them and thinking that they're not shifty or whatever.

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Speaker 3: This was then theorized in all sorts of ways by

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Speaker 3: various thinkers who argued that by feeling the outside of

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Speaker 3: your head skull, you could tell the shape of your

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Speaker 3: brain and the lumps on your head, because we've all

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Speaker 3: got lumps. They reveal the existence of organs and sections

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Speaker 3: of the brain that are doing particular things.

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Speaker 4: But rather than say, as.

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Speaker 3: You know, you might say, well they're devoted to speech

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Speaker 3: or you know, impulsiveness or vision, they were all about

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Speaker 3: moral virtues and you know, being goodness and so basically

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Speaker 3: it's like astrology, you know. And people would go and

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Speaker 3: it was incredibly popular in the nineteenth century, and the

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Speaker 3: scientists got really really cross about it because they said, look,

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Speaker 3: you know, your.

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Speaker 4: Skull's really thick.

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Speaker 3: You may have a big lump on your head, but

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Speaker 3: there's no lump corresponding lump underneath your brain. So this

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Speaker 3: is just rubbish. So this went on until the late

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Speaker 3: the early twentieth century. So this is pseudoscience, and despite

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Speaker 3: it being widely not accepted, but you know, ordinary people,

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Speaker 3: ordinary people from Queen Victoria to Carl Marx all thought

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Speaker 3: it was very interesting.

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Speaker 4: You know, there was nothing in it. It was complete,

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Speaker 4: not of rubbish.

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Speaker 3: But what it's got in there is this suggestion that

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Speaker 3: maybe there is localization of function. And this is eventually

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Speaker 3: resolved in about eighteen sixty when a French scientist called

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Speaker 3: Broker was studying the brains of patients who who'd have

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Speaker 3: strokes and have lost the power of speech, and he

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Speaker 3: found that consistently when he did the dissections, the front

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Speaker 3: left and area of the brain was damaged in these

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Speaker 3: patients who had lost the power of speech. Patients who

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Speaker 3: died and hadn't lost the power of speech did not

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Speaker 3: have those leeches legiance.

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Speaker 4: And he was really unhappy.

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Speaker 3: About this because he was French and this could not

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Speaker 3: be and yeah, I mean having I don't know if

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Speaker 3: this has happened to you, David, but you know, sometimes

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Speaker 3: you collect data and it.

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Speaker 4: Just pushes you into a corner.

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Speaker 3: You don't want to go to that corner because you

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Speaker 3: don't think that's the way it is. But the data

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Speaker 3: just in the end you've got no option. You're just

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Speaker 3: going to say, well, that's the way it is, this

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Speaker 3: is what happens. And poor old Broker had to say

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Speaker 3: and shock the French establishment by saying there is localization

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Speaker 3: of function. Of course, you can't see anything with the eye,

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Speaker 3: the naked eye for this front left hand side of

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Speaker 3: the brain. And then a couple and if you saw

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Speaker 3: this was a couple of years ago, quite astonishing paper

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Speaker 3: was produced between a group of neuroscientists were doing fMRI

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Speaker 3: scans and a woman in her thirties was a secretary

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Speaker 3: who had a college degree, who wrote to them. She

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Speaker 3: saw an advert, you know, saying we're looking for people

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Speaker 3: to have brain scanned. She said, I've been told there's

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Speaker 3: something interesting about my brain. And they said, okay, yeah,

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Speaker 3: come on in. They stuck her in the scanner and

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Speaker 3: the left hand brain side of her brain is completely empty.

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Speaker 3: There's nothing, there's a hemisphere completely and she has done

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Speaker 3: this ever since birth, and yet she can speak perfectly normally.

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Speaker 4: You would not know if you didn't use a scan out,

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Speaker 4: you'd have no idea.

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Speaker 1: I wrote about this in my book Live Wired. And

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Speaker 1: you know, you see the same thing. When a child

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Speaker 1: gets a hemisphere ectomy, they get half of their brain removed,

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Speaker 1: let's say, because of an intractable epilepsy.

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Speaker 2: Turns out they're fine.

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Speaker 1: And this is because the left and right sides of

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Speaker 1: the brain are sort of carbon copies of each other.

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Speaker 1: They're redundant for the most part, and so you can

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Speaker 1: take one out and the remaining hemisphere just rewires the

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Speaker 1: real estate to drive the boat as it needs.

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Speaker 4: To as long as you're very young. So it's a

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Speaker 4: plasticity thing, right.

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Speaker 1: They don't do hemispherectomies after about the age of eight typically, Yeah, yeah.

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Speaker 3: So, And there are examples of people who have had

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Speaker 3: unbelievable bad damage from strokes who then recovered their function

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Speaker 3: completely her adults. But as I'm always very careful to

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Speaker 3: say when I explain this to people, is this is

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Speaker 3: very rare and in general strokes are pretty bad news.

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Speaker 1: What's fascinating about that is sometimes somebody will get a

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Speaker 1: stroke on their left side that impacts Broker's area, let's say,

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Speaker 1: and they lose the ability to speak, maybe they get

404
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Speaker 1: a lesion of Wernikey's area, or they you know, they

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Speaker 1: lose the ability.

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Speaker 2: To produce language correctly, they have flu into phasia.

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Speaker 1: Okay, what happens is then they recover and people think, well, great,

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Speaker 1: somehow that has you know, they have repaired that part

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Speaker 1: of the brain. But what's in fact happened is it's

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Speaker 1: just moved over to the other hemisphere and now they're

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Speaker 1: taking care of language in their right hemisphere. And the

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Speaker 1: way this was discovered this was, you know, fifty years ago,

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Speaker 1: is then this poor person would let's say, have a

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Speaker 1: stroke on their right side and they would lose language again.

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Speaker 1: And so what this demonstrates is the massive plasticity the

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Speaker 1: ability to move stuff around to other territories on the

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Speaker 1: on the brain.

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Speaker 4: Which also shows us why the answer is it localized?

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Speaker 4: Man is is weird? So yes and no.

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Speaker 1: This is the difficulty that neuroscience has always faced with

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Speaker 1: the question about localization because sometimes, you know, if a

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Speaker 1: bomb drops on the runway of an airport, you'll notice

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Speaker 1: that all the planes stop, but you didn't hit the

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Speaker 1: airport itself, just the runway. And this is of course

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Speaker 1: what always happens with the brain. When we see brain

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Speaker 1: damage and some function stops. We don't know if that's

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Speaker 1: because that area was the function or just part of

428
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Speaker 1: this larger network that has to be there for things

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Speaker 1: to work for the planes to take off.

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Speaker 3: I mean, it's a very interesting philosophical and methodological problem

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Speaker 3: which people have been arguing about for decades, since since

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Speaker 3: the beginning of the twentieth century. And now, of course

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Speaker 3: we've got genetics, which does exactly the same thing. Or

434
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Speaker 3: I knocked out this gene it's the gene for X. Well,

435
00:24:52,720 --> 00:24:55,600
Speaker 3: very very occasionally it's the gene for X. But generally

436
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Speaker 3: you've just pulled apart a component, some bit of the

437
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Speaker 3: whole complicated network, and the whole thing falls apart, you know,

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Speaker 3: And it's very difficult to know when this is going

439
00:25:03,520 --> 00:25:06,119
Speaker 3: to happen. So analogies of things like bicycle wheels, you

440
00:25:06,160 --> 00:25:08,199
Speaker 3: take a spoke out, you take a spoke out, and

441
00:25:08,240 --> 00:25:10,320
Speaker 3: then eventually the wheel is going to collapse, but you

442
00:25:10,359 --> 00:25:12,760
Speaker 3: can't it's very difficult to predict when. And it doesn't

443
00:25:12,840 --> 00:25:15,760
Speaker 3: necessarily matter which order you take the spokes out or whatever.

444
00:25:15,920 --> 00:25:19,720
Speaker 3: So this is very hard and it's part of the

445
00:25:19,760 --> 00:25:22,080
Speaker 3: problem is it's easy for scientists to get excited about

446
00:25:22,080 --> 00:25:24,560
Speaker 3: this thing. I've got this tool, and it's easy for

447
00:25:24,600 --> 00:25:28,119
Speaker 3: the public to understand because it makes perfect sense. But

448
00:25:28,480 --> 00:25:30,439
Speaker 3: to get back to your starting point, to get to

449
00:25:30,480 --> 00:25:34,480
Speaker 3: the metaphor, if you ask the average person street you know,

450
00:25:34,560 --> 00:25:36,479
Speaker 3: what is the brain like a computer?

451
00:25:36,600 --> 00:25:39,040
Speaker 4: They go, yeah, well sure, but here's the thing.

452
00:25:39,160 --> 00:25:42,280
Speaker 3: You know, You've just explained that you know, strokes or whatever,

453
00:25:42,320 --> 00:25:45,520
Speaker 3: you can recover various elements from a function from them.

454
00:25:45,520 --> 00:25:48,760
Speaker 3: But if I take out the microphone from this computer,

455
00:25:48,880 --> 00:25:50,920
Speaker 3: you just won't hear me. There's no other part of it.

456
00:25:50,960 --> 00:25:52,320
Speaker 3: Is going to rewire it and go okay, well ill

457
00:25:52,359 --> 00:25:53,960
Speaker 3: use the camera instead. I mean, we're done.

458
00:25:54,400 --> 00:25:56,080
Speaker 4: There will be no more podcast.

459
00:25:56,560 --> 00:26:00,199
Speaker 3: And so that that fixity in a machine and the

460
00:26:00,240 --> 00:26:05,080
Speaker 3: flexibility of anything organic, but in particular the brain, that

461
00:26:05,200 --> 00:26:07,760
Speaker 3: just shows they are a different order of thing.

462
00:26:08,119 --> 00:26:09,640
Speaker 1: So, by the way, I want to return to this

463
00:26:09,720 --> 00:26:13,399
Speaker 1: issue about the metaphor of the brain as a computer,

464
00:26:13,480 --> 00:26:16,119
Speaker 1: because you pointed out something surprising in the book that

465
00:26:16,280 --> 00:26:19,399
Speaker 1: people were thinking about this the other way for a while.

466
00:26:19,480 --> 00:26:22,920
Speaker 1: When von Neuman was developing the computer tell us about that.

467
00:26:23,240 --> 00:26:25,760
Speaker 3: We all say the brain is like a computer. But

468
00:26:26,520 --> 00:26:29,840
Speaker 3: when the first computers, like we all use now, the

469
00:26:29,880 --> 00:26:32,840
Speaker 3: one with as you said, von Neumann architecture, when that

470
00:26:33,040 --> 00:26:35,360
Speaker 3: was being developed, what von Neuman said he was going

471
00:26:35,400 --> 00:26:37,200
Speaker 3: to do, and this is his pitch to the US

472
00:26:37,320 --> 00:26:40,080
Speaker 3: government in nineteen forty five to get the vast sums

473
00:26:40,080 --> 00:26:42,600
Speaker 3: of money to build this thing. Why did he she said,

474
00:26:42,600 --> 00:26:44,080
Speaker 3: I'm going to build you a computer and it's going

475
00:26:44,119 --> 00:26:46,080
Speaker 3: to be a computer like the brain.

476
00:26:46,280 --> 00:26:48,760
Speaker 4: And why do I say that? Because I know how

477
00:26:48,800 --> 00:26:49,800
Speaker 4: the brain's wired up.

478
00:26:49,880 --> 00:26:53,480
Speaker 3: How do I know that because two years earlier to

479
00:26:53,920 --> 00:26:57,399
Speaker 3: researchers McCullough, CA and Pits have published a paper on logic.

480
00:26:57,440 --> 00:26:59,760
Speaker 4: It was called the Imminent Logic of the Nervous System

481
00:27:00,080 --> 00:27:03,080
Speaker 4: in which they said, look the way the brain is wider.

482
00:27:03,200 --> 00:27:04,760
Speaker 4: All the nervous systems are wired up.

483
00:27:05,119 --> 00:27:07,959
Speaker 3: You've got things called synapses, which are where one neuron

484
00:27:08,040 --> 00:27:10,560
Speaker 3: sends a message on to another, and you can have

485
00:27:10,600 --> 00:27:11,600
Speaker 3: a straightforward relay.

486
00:27:11,800 --> 00:27:12,880
Speaker 4: That's not very interesting.

487
00:27:13,040 --> 00:27:16,320
Speaker 3: What gets interesting is if you've got some conditionality about

488
00:27:16,359 --> 00:27:19,879
Speaker 3: You could have a two neurons coming in and the

489
00:27:20,920 --> 00:27:25,840
Speaker 3: onward neuron will fire if both of them are inputs

490
00:27:25,840 --> 00:27:29,679
Speaker 3: are firing, or if one but not the other, and

491
00:27:29,720 --> 00:27:35,679
Speaker 3: these basic logical structures, if and then and not. Anybody

492
00:27:35,680 --> 00:27:38,600
Speaker 3: who's done any logic or any programming says, wait a minute,

493
00:27:38,680 --> 00:27:42,400
Speaker 3: I recognize that. And that's what McCulloch said, right, This

494
00:27:42,440 --> 00:27:45,159
Speaker 3: is astonishing. This is how nervous systems are wired up.

495
00:27:45,240 --> 00:27:47,879
Speaker 3: I'm going to build a computer like that now. I mean,

496
00:27:47,920 --> 00:27:51,360
Speaker 3: it's not true. Nervous systems are not wired up like that.

497
00:27:51,720 --> 00:27:55,119
Speaker 3: I mean, but it was a brilliant idea, and the

498
00:27:55,160 --> 00:27:58,800
Speaker 3: idea of that there's this logical structure that enabled our

499
00:27:58,840 --> 00:28:01,600
Speaker 3: brains or any brain function should produce an outcome to

500
00:28:01,640 --> 00:28:02,639
Speaker 3: process information.

501
00:28:03,280 --> 00:28:05,000
Speaker 4: And that's what von Neuman did.

502
00:28:05,080 --> 00:28:08,200
Speaker 3: That architecture is hardwired into the computers that I'm using

503
00:28:08,240 --> 00:28:09,800
Speaker 3: that you're all over the world.

504
00:28:09,920 --> 00:28:11,679
Speaker 4: Every computer in the world uses that.

505
00:28:12,040 --> 00:28:15,400
Speaker 3: Because he thought that mccullor ca and Pits had hit

506
00:28:15,440 --> 00:28:18,040
Speaker 3: on while they had hit on something, but it wasn't

507
00:28:18,080 --> 00:28:19,480
Speaker 3: particularly about neuroscience.

508
00:28:19,680 --> 00:28:22,960
Speaker 1: So let me step back for a second, because this idea,

509
00:28:23,160 --> 00:28:26,400
Speaker 1: this relationship between brains and computers, this is what we're

510
00:28:26,440 --> 00:28:28,760
Speaker 1: all very used to. But give us a sense of

511
00:28:28,800 --> 00:28:31,360
Speaker 1: the metaphors that people had used previously.

512
00:28:31,680 --> 00:28:35,280
Speaker 3: The ancient Greeks, I mean, they thought about either many

513
00:28:35,320 --> 00:28:39,800
Speaker 3: thought about something called numa pneuma. When you read this

514
00:28:40,040 --> 00:28:42,360
Speaker 3: ancient Greek stuff and you think, what is this?

515
00:28:42,600 --> 00:28:43,160
Speaker 4: Is it air?

516
00:28:43,520 --> 00:28:44,120
Speaker 2: Is it wind?

517
00:28:44,400 --> 00:28:48,040
Speaker 3: So there's this aerial phenomenon which they thought was what

518
00:28:48,280 --> 00:28:50,440
Speaker 3: was going on, but they didn't, I mean, and that

519
00:28:50,600 --> 00:28:53,960
Speaker 3: because you know, wind technology is the height of their technology.

520
00:28:54,040 --> 00:28:56,360
Speaker 4: That's what they had, you know, that's what powered ships

521
00:28:56,400 --> 00:28:57,000
Speaker 4: and so on.

522
00:28:57,320 --> 00:29:01,560
Speaker 3: Then The real breakthrough, I think came with Descartes, who

523
00:29:02,160 --> 00:29:06,479
Speaker 3: was in a Parisian park in the sixteen twenties wandering

524
00:29:06,520 --> 00:29:09,640
Speaker 3: around and they had a load of animatronics that these

525
00:29:09,680 --> 00:29:13,520
Speaker 3: statues which all work by hydraulics, which was pretty damn

526
00:29:13,600 --> 00:29:16,280
Speaker 3: cool and better in some respects and clockwork at the time.

527
00:29:17,000 --> 00:29:20,320
Speaker 4: And you know, you'd have a Hercules who would biff

528
00:29:21,120 --> 00:29:22,960
Speaker 4: a dragon on the head with a great being.

529
00:29:23,000 --> 00:29:24,960
Speaker 3: He had a club and he biffed this dragon. You know,

530
00:29:25,280 --> 00:29:28,040
Speaker 3: you go to a theme park and it was basically that.

531
00:29:28,240 --> 00:29:30,400
Speaker 3: But you know, I mean, just like you see old

532
00:29:30,480 --> 00:29:32,560
Speaker 3: CGI today and it looks crap. Mean, this would have

533
00:29:32,600 --> 00:29:34,800
Speaker 3: looked awful, but at the time it was amazing. And

534
00:29:34,960 --> 00:29:37,720
Speaker 3: the key thing is that descar I mean, he didn't

535
00:29:37,720 --> 00:29:39,520
Speaker 3: think they were alive, but he thought, wait a minute,

536
00:29:39,840 --> 00:29:42,960
Speaker 3: maybe that's how our bodies and our nervous systems work.

537
00:29:43,000 --> 00:29:45,400
Speaker 3: And he said, look, he's got this very famous drawing

538
00:29:45,440 --> 00:29:48,160
Speaker 3: of this big kind of man baby touching a fire

539
00:29:48,800 --> 00:29:51,520
Speaker 3: and he says, look, there's some kind of pressure from

540
00:29:51,560 --> 00:29:52,680
Speaker 3: when you touch flane.

541
00:29:52,800 --> 00:29:54,040
Speaker 4: The pressure goes up into the.

542
00:29:54,040 --> 00:29:59,080
Speaker 3: Brain and then it bounces back it reflects, hence eventually

543
00:29:59,280 --> 00:30:02,719
Speaker 3: reflex it bounces back and causes you to pull away

544
00:30:03,320 --> 00:30:03,960
Speaker 3: from the fire.

545
00:30:04,400 --> 00:30:07,720
Speaker 4: Now very quickly people tried to do experiments to see

546
00:30:07,720 --> 00:30:09,640
Speaker 4: whether this was the case. I mean, he constructed a

547
00:30:09,640 --> 00:30:10,480
Speaker 4: whole theory about it.

548
00:30:11,920 --> 00:30:14,880
Speaker 3: By within twenty thirty years and seventeenth century, people had

549
00:30:15,800 --> 00:30:18,880
Speaker 3: done fairly simple things like, you know, chopped a frog's

550
00:30:19,000 --> 00:30:22,480
Speaker 3: nerve and no liquid came spurting out, so it didn't

551
00:30:22,520 --> 00:30:25,320
Speaker 3: look like it was under some kind of hydraulic pressure.

552
00:30:25,560 --> 00:30:28,120
Speaker 3: People thought of other things. Maybe they thought it was

553
00:30:28,320 --> 00:30:31,680
Speaker 3: like a vibration. There's something going down the nerve. If

554
00:30:31,720 --> 00:30:33,560
Speaker 3: you hit a plank at one end, you can feel

555
00:30:33,560 --> 00:30:36,600
Speaker 3: the vibration at the other, which's pretty smart, but there's

556
00:30:36,640 --> 00:30:38,680
Speaker 3: not much you can do about it, and thinking about,

557
00:30:38,720 --> 00:30:40,560
Speaker 3: you know, a load of vibrating planks in your head

558
00:30:40,600 --> 00:30:42,120
Speaker 3: doesn't really help you much.

559
00:30:42,560 --> 00:30:47,800
Speaker 4: So even descartes idea wasn't really usable, but that was

560
00:30:47,960 --> 00:30:49,160
Speaker 4: kind of how it was thought.

561
00:30:49,240 --> 00:30:53,120
Speaker 3: And then with electricity, the big breakthrough wasn't just that

562
00:30:53,200 --> 00:30:58,000
Speaker 3: electricity is what's in this weird chemical way in neurons,

563
00:30:58,160 --> 00:31:01,200
Speaker 3: But as soon as that was applied in the shape

564
00:31:01,240 --> 00:31:06,560
Speaker 3: of the telegraph system, then people immediately drew an analogy

565
00:31:06,760 --> 00:31:10,400
Speaker 3: both ways. They said look, here's there are maps the

566
00:31:10,520 --> 00:31:14,560
Speaker 3: telegraph system in the UK in the eighteen forties with

567
00:31:14,880 --> 00:31:17,800
Speaker 3: all the telegraph wires which were all running along railway

568
00:31:17,840 --> 00:31:21,960
Speaker 3: linees course, going down to London. And what people said

569
00:31:22,080 --> 00:31:26,600
Speaker 3: is London is like the brain of the country and

570
00:31:26,760 --> 00:31:28,240
Speaker 3: it receives information.

571
00:31:28,480 --> 00:31:29,440
Speaker 4: This is a word they use.

572
00:31:29,480 --> 00:31:32,640
Speaker 3: It's information from the provinces and it can also send

573
00:31:32,720 --> 00:31:37,040
Speaker 3: instructions out and tell bits of the body politic what

574
00:31:37,240 --> 00:31:39,400
Speaker 3: to do. And hey, we look at the nervous system,

575
00:31:39,400 --> 00:31:42,120
Speaker 3: because they had fabulous dissections of the human nervous system.

576
00:31:42,160 --> 00:31:44,800
Speaker 3: It's all going up into the brain and motor uron's

577
00:31:44,840 --> 00:31:47,840
Speaker 3: coming down, et cetera. So this parallel was now we've

578
00:31:47,840 --> 00:31:50,360
Speaker 3: gone from water that don't really make much sense or

579
00:31:50,480 --> 00:31:55,240
Speaker 3: something electric. Now we've got a real communication metaphor of

580
00:31:55,840 --> 00:32:00,400
Speaker 3: the telegraph system. And this chap called Alfred Smith, he

581
00:32:01,000 --> 00:32:03,680
Speaker 3: was absolutely convinced that literally this was what was going

582
00:32:03,760 --> 00:32:07,160
Speaker 3: on inside the nervous system. And he even invented the

583
00:32:07,280 --> 00:32:11,360
Speaker 3: facts or a kind of telegraph, the photo.

584
00:32:11,600 --> 00:32:12,440
Speaker 4: He said, if you could get.

585
00:32:12,320 --> 00:32:14,520
Speaker 3: A photo electric cell and send a man image down

586
00:32:14,560 --> 00:32:16,800
Speaker 3: a telegraph, why you could should be able to reproduce

587
00:32:16,840 --> 00:32:18,640
Speaker 3: it at the other end. So you'd actually get and

588
00:32:18,720 --> 00:32:20,480
Speaker 3: that's what's going on in your eyes, he said, going

589
00:32:20,520 --> 00:32:23,440
Speaker 3: into your brain. And he drew these amazing diagrams which

590
00:32:23,440 --> 00:32:25,320
Speaker 3: and I show them to computation neuroscientists.

591
00:32:25,320 --> 00:32:28,120
Speaker 4: They get so excited because it looks like a primitive

592
00:32:28,240 --> 00:32:31,080
Speaker 4: version of you know, the large language models that.

593
00:32:31,440 --> 00:32:35,479
Speaker 3: Everybody's excited about these days, all these crisscross interactions. I mean,

594
00:32:35,520 --> 00:32:37,480
Speaker 3: I've tried to make head and tail of what he

595
00:32:37,560 --> 00:32:39,800
Speaker 3: was getting at, and I think he was basically bonkers.

596
00:32:39,840 --> 00:32:43,760
Speaker 3: And he hads to have looked. It looks it looks familiar,

597
00:32:43,960 --> 00:32:47,080
Speaker 3: but it was actually insane. He made it, made a

598
00:32:47,160 --> 00:32:50,320
Speaker 3: model of the brain out of brass, these strange bits

599
00:32:50,360 --> 00:32:52,920
Speaker 3: of brass that he shows in his drawings, and you

600
00:32:53,040 --> 00:32:54,720
Speaker 3: look at it and think, I don't you know what

601
00:32:54,840 --> 00:32:58,720
Speaker 3: could this do? How could this process information in any

602
00:32:58,800 --> 00:33:01,520
Speaker 3: meaningful way? And there so you know, we got electricity.

603
00:33:01,600 --> 00:33:04,720
Speaker 3: And then in the eighteen eighties you've got the problem

604
00:33:04,760 --> 00:33:07,720
Speaker 3: about telegraph wise is it's just the static. They don't change,

605
00:33:08,000 --> 00:33:10,320
Speaker 3: whereas we know that the brain has to respond to

606
00:33:10,360 --> 00:33:12,800
Speaker 3: all sorts of different signals. And then the big change

607
00:33:13,320 --> 00:33:17,520
Speaker 3: was the advent of the telephone and the telephone exchange

608
00:33:17,640 --> 00:33:19,800
Speaker 3: because younger listeners.

609
00:33:19,440 --> 00:33:21,280
Speaker 4: Will have no idea what one of these is.

610
00:33:21,360 --> 00:33:25,280
Speaker 3: There's there's something called a telephone exchange where what would

611
00:33:25,320 --> 00:33:28,000
Speaker 3: happen is you pick up your phone. You can see

612
00:33:28,000 --> 00:33:30,520
Speaker 3: this in old silent movies on YouTube. You pick up

613
00:33:30,560 --> 00:33:33,760
Speaker 3: your phone and there's a telephone operator who was normally

614
00:33:33,840 --> 00:33:36,800
Speaker 3: a woman in the exchange, and she would a light

615
00:33:36,880 --> 00:33:38,360
Speaker 3: would come on saying you picked up your phone.

616
00:33:38,400 --> 00:33:41,760
Speaker 4: She would connect a wire to your light, and you would.

617
00:33:41,560 --> 00:33:44,280
Speaker 3: Then ask for a particular number, and she would then

618
00:33:44,360 --> 00:33:47,240
Speaker 3: connect the other end of that wire to that number.

619
00:33:47,960 --> 00:33:50,880
Speaker 3: So you've got this flexibility. It's a switchboard, and I

620
00:33:50,960 --> 00:33:52,920
Speaker 3: mean it's not bad. I mean it is in a

621
00:33:53,000 --> 00:33:56,240
Speaker 3: way because you're rerouting information. There's nothing permanent, there's no

622
00:33:56,400 --> 00:34:00,520
Speaker 3: fixed connections that can all be flexible. And that carries

623
00:34:00,600 --> 00:34:05,320
Speaker 3: on into the First World War, and that's where ideas

624
00:34:05,320 --> 00:34:10,680
Speaker 3: about information start to appear. Before this is mathematized, people

625
00:34:10,840 --> 00:34:13,319
Speaker 3: started to use the word information. In particular chap called

626
00:34:13,440 --> 00:34:17,080
Speaker 3: Edgar Adrian, who's beloved of electrophysiologists, but unknown to most people.

627
00:34:17,320 --> 00:34:19,759
Speaker 3: He won the Nobel Prize. Two of his students won

628
00:34:19,800 --> 00:34:22,680
Speaker 3: the Nobel Prize. He was the president of the Royal Society.

629
00:34:22,719 --> 00:34:24,759
Speaker 3: He was the vice chancellor of Cambridge University. I mean

630
00:34:24,800 --> 00:34:28,600
Speaker 3: you could not get greater accolades than that. And now

631
00:34:29,000 --> 00:34:31,480
Speaker 3: nobody knows who he is apart from the students at

632
00:34:31,480 --> 00:34:34,160
Speaker 3: the University of Lesdoo got an Adrian building. Anyway, he

633
00:34:34,280 --> 00:34:38,160
Speaker 3: writes these popular science books and he tries to show

634
00:34:38,719 --> 00:34:42,080
Speaker 3: to explain to people what's happening. His first person to

635
00:34:42,120 --> 00:34:44,760
Speaker 3: record from a single neuron, to record a single spike.

636
00:34:44,840 --> 00:34:47,759
Speaker 3: You can see it going down there as you described it.

637
00:34:48,280 --> 00:34:52,120
Speaker 3: And he also shows a spike is either on or off.

638
00:34:52,200 --> 00:34:54,560
Speaker 3: You either have it or you don't, so it's all

639
00:34:54,719 --> 00:34:58,520
Speaker 3: or none, as it said. What the nervous system does

640
00:34:58,640 --> 00:35:02,520
Speaker 3: with the information of spikes is to actually group them together.

641
00:35:03,360 --> 00:35:06,080
Speaker 3: And it's an in the code as he called it.

642
00:35:06,160 --> 00:35:09,440
Speaker 3: He talked about codes in nervous systems, so are codes

643
00:35:09,520 --> 00:35:12,360
Speaker 3: carrying information. And he has this lovely diagram of a

644
00:35:12,960 --> 00:35:15,360
Speaker 3: again another poor old frog. It's the end of a

645
00:35:15,600 --> 00:35:18,640
Speaker 3: stretch a neuron which is attached to a stretch receptor.

646
00:35:18,719 --> 00:35:21,239
Speaker 3: It's a stretch receptor attached to a muscle, and he

647
00:35:21,320 --> 00:35:24,000
Speaker 3: puts increasing weights on it. And as he puts more

648
00:35:24,000 --> 00:35:27,120
Speaker 3: and more weights, the cell stretches and you get more

649
00:35:27,320 --> 00:35:30,920
Speaker 3: and more spikes, so it's still it's digital in the

650
00:35:31,000 --> 00:35:34,279
Speaker 3: sense it's all or nothing each of those spikes. But

651
00:35:34,440 --> 00:35:37,640
Speaker 3: what the nervous system is seeing is not zeros and ones.

652
00:35:38,040 --> 00:35:41,480
Speaker 3: It's seeing an intensity code and a frequency code. How

653
00:35:41,520 --> 00:35:44,399
Speaker 3: many spikes have there been a second perhaps even how

654
00:35:44,480 --> 00:35:46,319
Speaker 3: they organized within a unit time.

655
00:35:46,400 --> 00:35:48,960
Speaker 4: These are all stuff that people are still very interested in.

656
00:35:49,400 --> 00:35:52,560
Speaker 3: So then when you finally get the computer all the

657
00:35:52,960 --> 00:35:56,120
Speaker 3: in the mid late forties, early fifties, it all kinds

658
00:35:56,120 --> 00:35:57,040
Speaker 3: of starts to make sense.

659
00:35:57,120 --> 00:36:02,000
Speaker 4: Yes, and this is where we this is part of

660
00:36:02,120 --> 00:36:05,160
Speaker 4: my You know, we haven't gone any further, right, I

661
00:36:05,239 --> 00:36:23,120
Speaker 4: don't think, so let me ask you this.

662
00:36:23,760 --> 00:36:26,040
Speaker 2: When I read your book, I was curious your opinion

663
00:36:26,120 --> 00:36:26,279
Speaker 2: on this.

664
00:36:26,440 --> 00:36:30,200
Speaker 1: So you published this in twenty twenty, and since that time,

665
00:36:30,719 --> 00:36:34,960
Speaker 1: we've had this explosion with the transformer architecture and large

666
00:36:35,040 --> 00:36:38,359
Speaker 1: language models, which have changed the game a little bit,

667
00:36:38,440 --> 00:36:40,359
Speaker 1: by which I mean, I think this is probably true

668
00:36:40,400 --> 00:36:44,800
Speaker 1: for both of us. You know, our whole career in neuroscience,

669
00:36:44,880 --> 00:36:48,279
Speaker 1: we always looked at AI and sort of snickered at

670
00:36:48,320 --> 00:36:50,160
Speaker 1: the idea that it was going to be anything as

671
00:36:50,200 --> 00:36:52,719
Speaker 1: good as the brain, And then suddenly just a few

672
00:36:52,800 --> 00:36:56,320
Speaker 1: years ago, it's got quite extraordinary, and so I'm curious

673
00:36:56,400 --> 00:36:59,839
Speaker 1: how you think that changes the metaphors that we're using now.

674
00:37:00,239 --> 00:37:02,759
Speaker 1: And I think we would probably agree that there's never

675
00:37:02,880 --> 00:37:04,720
Speaker 1: going to be a perfect metaphor.

676
00:37:05,760 --> 00:37:08,440
Speaker 2: But how do you think LM's change the way we're

677
00:37:08,480 --> 00:37:09,200
Speaker 2: talking about things?

678
00:37:09,440 --> 00:37:11,480
Speaker 4: Well, honestly, how they can help? How do they work?

679
00:37:12,280 --> 00:37:13,040
Speaker 2: Well, they don't know.

680
00:37:13,800 --> 00:37:16,600
Speaker 4: Yeah, so if you have Jeffrey Hinton, how does it work?

681
00:37:16,680 --> 00:37:18,040
Speaker 4: He goes, I don't know.

682
00:37:18,600 --> 00:37:20,719
Speaker 2: So this is a really interesting thing though.

683
00:37:20,840 --> 00:37:25,520
Speaker 1: So for example, you and I both are interested in,

684
00:37:25,640 --> 00:37:29,640
Speaker 1: let's say Eve Martyr's work where she tries to describe

685
00:37:29,760 --> 00:37:30,280
Speaker 1: the lobster.

686
00:37:31,160 --> 00:37:32,200
Speaker 4: The example I give.

687
00:37:32,080 --> 00:37:34,800
Speaker 1: You great, Okay, great, so well here, why don't you

688
00:37:34,800 --> 00:37:36,520
Speaker 1: go ahead and give it about Eve Martyr. But the

689
00:37:36,600 --> 00:37:39,400
Speaker 1: point I want to make is that the questions that

690
00:37:39,640 --> 00:37:43,640
Speaker 1: remain about what's happening in neuroscience, we have the same

691
00:37:43,760 --> 00:37:46,120
Speaker 1: explainability crisis with lllm's.

692
00:37:46,360 --> 00:37:47,799
Speaker 2: So they're not distinguished in that way.

693
00:37:48,360 --> 00:37:52,800
Speaker 3: No, indeed, absolutely right, But rather it's so the example

694
00:37:52,880 --> 00:37:54,960
Speaker 3: in the Eve martt so, yes, it's the lobster's stomach.

695
00:37:55,080 --> 00:37:56,839
Speaker 3: She has spent a whole less a very smart woman

696
00:37:56,880 --> 00:38:00,680
Speaker 3: spent her whole academic career studying the thirteen years that

697
00:38:01,200 --> 00:38:04,719
Speaker 3: make up the system that controls them the activity of

698
00:38:04,719 --> 00:38:07,200
Speaker 3: the lobster's stomach, and the lobster's stomach has to grind

699
00:38:07,280 --> 00:38:09,520
Speaker 3: up its food a bit like a gizzard in a chicken,

700
00:38:10,000 --> 00:38:13,160
Speaker 3: and it's got two rhythms, two ways of doing this,

701
00:38:13,320 --> 00:38:15,879
Speaker 3: and this is controlled by the neurons. And she knows

702
00:38:16,080 --> 00:38:20,560
Speaker 3: everything about those neurons. She knows the genes that are expressed,

703
00:38:20,640 --> 00:38:24,400
Speaker 3: she knows all about the neurotransmitters, the neural hormones that

704
00:38:24,520 --> 00:38:27,440
Speaker 3: they bathe in that kind of modulate the way that

705
00:38:27,560 --> 00:38:30,840
Speaker 3: it all activates. But at the moment, she cannot explain,

706
00:38:31,000 --> 00:38:34,000
Speaker 3: using the tools we've had up to now, why those

707
00:38:34,080 --> 00:38:37,880
Speaker 3: thirty neurons produce those two rhythms and not others, because

708
00:38:37,960 --> 00:38:40,000
Speaker 3: according to the models, they should be able to do

709
00:38:40,080 --> 00:38:44,479
Speaker 3: other things, but they don't. And she can't predict, either

710
00:38:44,840 --> 00:38:47,360
Speaker 3: experimentally or with a model, or by a conjunction of

711
00:38:47,360 --> 00:38:49,479
Speaker 3: the two, what will happen if she is to alter

712
00:38:49,520 --> 00:38:52,279
Speaker 3: the activity of one of those components. So, yeah, I mean,

713
00:38:52,320 --> 00:38:54,680
Speaker 3: I take your point. This is, you know, we've got

714
00:38:54,760 --> 00:38:59,960
Speaker 3: two completely inexplicable things we don't understand what's going on

715
00:39:00,200 --> 00:39:03,280
Speaker 3: in the LLLM. But that's partly because the way it's built.

716
00:39:03,400 --> 00:39:06,239
Speaker 1: It feels like the LM should be so straightforward to

717
00:39:06,320 --> 00:39:09,279
Speaker 1: understand in the sense that we've put it in there.

718
00:39:09,360 --> 00:39:11,680
Speaker 1: It's a cartoon model of the brain in the sense

719
00:39:11,680 --> 00:39:14,400
Speaker 1: that it's just units and connections between the units. And

720
00:39:14,960 --> 00:39:18,440
Speaker 1: what this illustrates, I think is once things reach some

721
00:39:18,640 --> 00:39:22,080
Speaker 1: level of complexity, it's not clear that we have the

722
00:39:22,200 --> 00:39:26,480
Speaker 1: correct metaphors or the correct tools and science to capture

723
00:39:26,520 --> 00:39:27,120
Speaker 1: it in some way.

724
00:39:27,320 --> 00:39:27,520
Speaker 4: Yeah.

725
00:39:27,600 --> 00:39:30,640
Speaker 3: I mean, like everybody, I'm amazed by what the LLMS

726
00:39:30,719 --> 00:39:35,880
Speaker 3: can do. But I mean today, may this will all

727
00:39:35,960 --> 00:39:38,360
Speaker 3: be old news by the time this is released. But

728
00:39:38,560 --> 00:39:41,000
Speaker 3: today the big thing is chut GPT fives come out

729
00:39:41,480 --> 00:39:44,239
Speaker 3: and it still doesn't know how many bees there are

730
00:39:44,280 --> 00:39:47,400
Speaker 3: in blueberry, right, And it gets very indignant and insists

731
00:39:47,440 --> 00:39:50,080
Speaker 3: that there are three because the capital letter counts for two,

732
00:39:50,320 --> 00:39:53,359
Speaker 3: or because the bee in the middle for berry counts

733
00:39:53,400 --> 00:39:53,640
Speaker 3: for two.

734
00:39:53,680 --> 00:39:55,360
Speaker 4: I mean, you know, so people are arguing with it,

735
00:39:55,560 --> 00:39:57,160
Speaker 4: and this thing sounds real.

736
00:39:57,400 --> 00:39:59,719
Speaker 3: It sounds that it knows what it's talking about, you know,

737
00:40:00,200 --> 00:40:04,000
Speaker 3: So it's that's very confident.

738
00:40:04,120 --> 00:40:07,280
Speaker 1: Here's what seems clear though, is that they are intelligent

739
00:40:07,400 --> 00:40:11,320
Speaker 1: in a different way, as in, they get some simple

740
00:40:11,400 --> 00:40:14,680
Speaker 1: things wrong, but they also do quite extraordinary other things

741
00:40:14,760 --> 00:40:15,680
Speaker 1: that we're no good at.

742
00:40:16,160 --> 00:40:19,960
Speaker 2: So it's like we've invented a species that it is

743
00:40:20,040 --> 00:40:21,239
Speaker 2: just a bit different from us.

744
00:40:21,440 --> 00:40:24,600
Speaker 4: So that is that true? I mean, you know you

745
00:40:24,680 --> 00:40:26,759
Speaker 4: can why is it intelligent?

746
00:40:26,800 --> 00:40:28,759
Speaker 3: I mean you've got any Any machine that you have

747
00:40:29,760 --> 00:40:32,400
Speaker 3: enables you to do things that you can't do yourself.

748
00:40:32,480 --> 00:40:35,080
Speaker 3: I mean, draw a straight line, right, I can draw

749
00:40:35,160 --> 00:40:37,400
Speaker 3: straight line on my computer in a way that I

750
00:40:37,480 --> 00:40:40,680
Speaker 3: can't with my hand and a pencil, and I can

751
00:40:40,719 --> 00:40:42,360
Speaker 3: do it better with a pencil than I can with

752
00:40:42,680 --> 00:40:43,040
Speaker 3: a rock.

753
00:40:43,719 --> 00:40:44,080
Speaker 2: That's right.

754
00:40:44,160 --> 00:40:46,560
Speaker 1: But I think the surprise has been for us the

755
00:40:46,680 --> 00:40:49,600
Speaker 1: way that it can put ideas together in ways that

756
00:40:49,880 --> 00:40:53,800
Speaker 1: had seemed just a few years ago like some magical

757
00:40:53,920 --> 00:40:55,360
Speaker 1: thing that only humans could do.

758
00:40:55,560 --> 00:40:58,080
Speaker 2: Writing a pair of you can ask you look, Jimmy.

759
00:40:57,880 --> 00:41:00,640
Speaker 1: A shakespeareance on it, where you know, poke Kiemon meets

760
00:41:00,680 --> 00:41:02,880
Speaker 1: Godzilla on the surface of Mars, and it does it

761
00:41:03,000 --> 00:41:08,080
Speaker 1: instantly and extraordinarily, And these sorts of things we would

762
00:41:08,120 --> 00:41:08,360
Speaker 1: not have.

763
00:41:08,440 --> 00:41:10,040
Speaker 2: Expected even just a few years ago.

764
00:41:10,480 --> 00:41:12,440
Speaker 3: Yeah, and we probably would have expected they'd know how

765
00:41:12,480 --> 00:41:13,879
Speaker 3: many bays they were in blue booth.

766
00:41:14,000 --> 00:41:16,400
Speaker 2: Yeah, exactly, so it's doing something.

767
00:41:16,680 --> 00:41:19,959
Speaker 1: But here's my question for you, is the metaphor has

768
00:41:20,320 --> 00:41:23,239
Speaker 1: changed a bit just the last few years from von

769
00:41:23,320 --> 00:41:28,200
Speaker 1: Neumann computers to having these large language models. That change

770
00:41:28,239 --> 00:41:31,120
Speaker 1: a little bit the way we think about what might

771
00:41:31,239 --> 00:41:33,440
Speaker 1: be happening in the brain, whether or not it's correct

772
00:41:33,920 --> 00:41:35,120
Speaker 1: that that's what's happening in the brain.

773
00:41:35,280 --> 00:41:38,560
Speaker 2: For young people who are growing up right now thinking.

774
00:41:38,320 --> 00:41:41,400
Speaker 1: About large language models and thinking about the brain, the

775
00:41:41,560 --> 00:41:44,560
Speaker 1: metaphors are going to carry over in terms of Okay,

776
00:41:44,600 --> 00:41:47,399
Speaker 1: what if you just have a giant network with lots

777
00:41:47,440 --> 00:41:50,800
Speaker 1: of connections and you can pay attention in the network

778
00:41:51,160 --> 00:41:55,800
Speaker 1: to certain words. That's going to be the next crop

779
00:41:55,880 --> 00:41:58,680
Speaker 1: of scientists are all going to draw on that metaphor.

780
00:41:58,920 --> 00:42:01,480
Speaker 4: I mean, in a way we've been here for a

781
00:42:01,560 --> 00:42:01,920
Speaker 4: long time.

782
00:42:02,040 --> 00:42:05,399
Speaker 3: Francis Crick, as everybody knows, co discover the double heading

783
00:42:05,400 --> 00:42:08,080
Speaker 3: structure of the DNA in nineteen fifty three with Jim

784
00:42:08,120 --> 00:42:10,160
Speaker 3: Watson on the basis of work that been done by

785
00:42:10,520 --> 00:42:13,879
Speaker 3: Maurice Wilkins and Russell and Franklin, and then he worked

786
00:42:13,920 --> 00:42:16,880
Speaker 3: in molecular genetics and then in the mid seventies he

787
00:42:16,960 --> 00:42:20,480
Speaker 3: decides he's going to move into neuroscience, and then I'm

788
00:42:20,560 --> 00:42:25,680
Speaker 3: working on the book, and always in every chapter except

789
00:42:25,719 --> 00:42:30,120
Speaker 3: the chapter on neurotransmitters, Crick popped up. And he wasn't

790
00:42:30,200 --> 00:42:32,440
Speaker 3: just popping up as you know, to somebody who was

791
00:42:32,520 --> 00:42:36,640
Speaker 3: on the fringes. He was actually driving ideas and thinking

792
00:42:36,920 --> 00:42:39,560
Speaker 3: and so on. In particular I was particularly impressed by

793
00:42:40,239 --> 00:42:44,719
Speaker 3: was his embracing of something that was called parallel distributed programming,

794
00:42:45,280 --> 00:42:50,359
Speaker 3: which was developed by Jeffrey Hinton and others in San

795
00:42:50,400 --> 00:42:54,400
Speaker 3: Diego and around San Diego in California in the mid eighties,

796
00:42:54,520 --> 00:42:57,120
Speaker 3: which is the ancestor of today's LMS.

797
00:42:57,360 --> 00:42:58,880
Speaker 4: He knew John Hotfield and hot.

798
00:42:58,760 --> 00:43:01,840
Speaker 3: Field and Hint of just won the Nobel Prize in

799
00:43:01,920 --> 00:43:06,080
Speaker 3: Physics last year for their work on these neural networks

800
00:43:06,400 --> 00:43:09,000
Speaker 3: and all the rest of it. And people were amazed

801
00:43:09,040 --> 00:43:11,480
Speaker 3: by what these very very primitive neural networks could do.

802
00:43:11,600 --> 00:43:14,480
Speaker 3: Because there'd been something called the AI winter from the

803
00:43:14,520 --> 00:43:17,720
Speaker 3: excitement of the nineteen fifties. In the nineteen sixties and seventies,

804
00:43:17,760 --> 00:43:19,839
Speaker 3: people thought, actually, this isn't just isn't going to work,

805
00:43:20,000 --> 00:43:20,719
Speaker 3: and you.

806
00:43:20,719 --> 00:43:23,680
Speaker 4: Know, no funding FREEI was a disaster.

807
00:43:24,040 --> 00:43:26,799
Speaker 3: And then these tiny little programs were developed which could

808
00:43:26,800 --> 00:43:30,800
Speaker 3: do things like learn the past tense is of English verbs,

809
00:43:31,120 --> 00:43:31,440
Speaker 3: and they.

810
00:43:31,360 --> 00:43:33,000
Speaker 4: Would make mistakes like a human child.

811
00:43:33,040 --> 00:43:36,720
Speaker 3: That's so even if you told that told the computer

812
00:43:36,840 --> 00:43:40,719
Speaker 3: that the past participle of go is went, it would

813
00:43:40,800 --> 00:43:43,560
Speaker 3: say God, just like child. That's you know, so it's

814
00:43:43,640 --> 00:43:51,040
Speaker 3: generalizing the e the most frequently accounted version, even though

815
00:43:51,239 --> 00:43:54,680
Speaker 3: it's been told differently. So that got people are very excited,

816
00:43:54,800 --> 00:43:58,040
Speaker 3: and Crick throughout this period was very impressed by these things,

817
00:43:58,080 --> 00:44:02,680
Speaker 3: but he was continually arguing people saying, look, your models

818
00:44:02,920 --> 00:44:06,240
Speaker 3: are amazing, but as models of the brain, they've.

819
00:44:06,080 --> 00:44:07,560
Speaker 4: Got to be biologically relevant.

820
00:44:07,560 --> 00:44:10,600
Speaker 3: They've got to be structured've got to have an organization,

821
00:44:11,280 --> 00:44:13,600
Speaker 3: and that is similar to what's going on in the brain,

822
00:44:13,640 --> 00:44:16,200
Speaker 3: which we didn't have much idea about. And if it

823
00:44:16,360 --> 00:44:19,920
Speaker 3: relies upon, in particular, something called back propagation, which is

824
00:44:19,960 --> 00:44:22,480
Speaker 3: where the signal comes back and goes up through the

825
00:44:22,560 --> 00:44:28,120
Speaker 3: same connections in these computer programs, these connections equipment of neurons,

826
00:44:28,120 --> 00:44:30,640
Speaker 3: and there isn't any backprop you know, it doesn't work

827
00:44:30,680 --> 00:44:32,160
Speaker 3: that way. And yes, you know work arounds, but that's

828
00:44:32,200 --> 00:44:34,440
Speaker 3: not the way it works. He got very irate with

829
00:44:35,000 --> 00:44:38,279
Speaker 3: his erstwhile colleagues and friends because of that, And that's

830
00:44:38,680 --> 00:44:41,160
Speaker 3: clearly right. You know, if you want to understand the

831
00:44:41,239 --> 00:44:46,000
Speaker 3: human brain rather than perhaps you know, understand the eerie

832
00:44:46,320 --> 00:44:50,080
Speaker 3: alien intelligence, if you want to stand human consciousness, then

833
00:44:50,160 --> 00:44:51,719
Speaker 3: you've got to keep that in mind, I think, and

834
00:44:51,840 --> 00:44:55,000
Speaker 3: crit was right about that. So until we need to

835
00:44:55,120 --> 00:44:57,480
Speaker 3: know how they're doing what they're doing, I think for

836
00:44:57,560 --> 00:45:00,840
Speaker 3: it to be a really useful model.

837
00:45:01,680 --> 00:45:02,879
Speaker 2: So okay, a couple of things.

838
00:45:02,960 --> 00:45:06,960
Speaker 1: First, I still assert that the lms are going to

839
00:45:07,280 --> 00:45:10,239
Speaker 1: change our metaphors the way that people of the next

840
00:45:10,280 --> 00:45:14,440
Speaker 1: generation talk about the brain, correctly or incorrectly.

841
00:45:14,520 --> 00:45:16,640
Speaker 2: It's going to change the way they talk about it.

842
00:45:16,760 --> 00:45:20,759
Speaker 1: And and of course there's a history back to you know,

843
00:45:20,880 --> 00:45:24,759
Speaker 1: parallel distributed computation and well before that as well. But

844
00:45:25,160 --> 00:45:27,400
Speaker 1: but there are step changes, and I think we've just

845
00:45:27,480 --> 00:45:30,120
Speaker 1: experienced one. So that's that's my assertion.

846
00:45:30,200 --> 00:45:31,719
Speaker 4: I was just wondering, if you know, I mean, you

847
00:45:32,080 --> 00:45:34,279
Speaker 4: may well be right. You know, I can't. I I

848
00:45:34,840 --> 00:45:38,160
Speaker 4: it'd be very interesting to see, and I just want

849
00:45:38,200 --> 00:45:39,120
Speaker 4: a bit more meat on it.

850
00:45:40,320 --> 00:45:42,400
Speaker 1: Yeah, you know, it may it may be that it

851
00:45:42,560 --> 00:45:45,600
Speaker 1: just requires some years before we see what that what

852
00:45:45,719 --> 00:45:48,040
Speaker 1: that meat looks like on the book. Yeah, let me

853
00:45:48,160 --> 00:45:51,040
Speaker 1: jump to another topic that I was that I loved

854
00:45:51,080 --> 00:45:53,640
Speaker 1: reading about in your book. One of the things that

855
00:45:53,719 --> 00:45:56,239
Speaker 1: I talk about on this podcast, probably more than anything else,

856
00:45:56,360 --> 00:45:59,160
Speaker 1: is this concept of the internal model. In other words,

857
00:45:59,239 --> 00:46:01,719
Speaker 1: your brain is locked in silence and darkness and it's

858
00:46:01,840 --> 00:46:03,960
Speaker 1: making a model of the outside world. And it does

859
00:46:04,040 --> 00:46:06,960
Speaker 1: this eventually so it can better predict what's going to

860
00:46:07,600 --> 00:46:11,000
Speaker 1: happen next. And the thing that I hadn't quite clocked

861
00:46:11,040 --> 00:46:14,000
Speaker 1: until I read your book is that, at least the

862
00:46:14,040 --> 00:46:15,719
Speaker 1: way that you put it, it's sort of correct me

863
00:46:15,760 --> 00:46:19,760
Speaker 1: if I'm wrong here. It started with the young Craike

864
00:46:20,160 --> 00:46:24,479
Speaker 1: in England who wrote a paper, and then other people

865
00:46:24,560 --> 00:46:26,239
Speaker 1: picked up on this idea. Give us, give us that

866
00:46:26,320 --> 00:46:27,720
Speaker 1: story about the internal model.

867
00:46:28,440 --> 00:46:31,400
Speaker 3: It really goes back to Helmholtz, who's the guy who

868
00:46:31,400 --> 00:46:36,479
Speaker 3: discovers the all or nothing action potential. What Helmholtz said

869
00:46:37,120 --> 00:46:41,080
Speaker 3: is that because there's a big argument about ah, is

870
00:46:41,160 --> 00:46:45,879
Speaker 3: the activity in say, your optic nerve, is it qualitatively

871
00:46:46,000 --> 00:46:50,640
Speaker 3: different from the activity in your auditory nerve? And what

872
00:46:51,000 --> 00:46:54,759
Speaker 3: Helmholtz said, it's all the same. It's just activity. And

873
00:46:54,920 --> 00:46:57,960
Speaker 3: the difference is is what your brain makes of that signal.

874
00:46:58,400 --> 00:47:01,759
Speaker 3: And what he says is that the brain gets stimulated

875
00:47:01,880 --> 00:47:05,440
Speaker 3: and then it makes an inference about what that means,

876
00:47:05,560 --> 00:47:07,680
Speaker 3: about what that is, and you can you can see

877
00:47:07,719 --> 00:47:10,239
Speaker 3: this really easily, but don't do it very hard. The

878
00:47:10,360 --> 00:47:14,000
Speaker 3: pressure eyeballs. Shut your eyes, pressure eyeballs, and you see colors,

879
00:47:14,960 --> 00:47:17,680
Speaker 3: but there is no light. What is happening is those

880
00:47:17,760 --> 00:47:21,080
Speaker 3: neurons are being activated by pressure and they're sending a

881
00:47:21,120 --> 00:47:23,480
Speaker 3: signal to the brain and the brain, hey, I optic

882
00:47:23,640 --> 00:47:26,560
Speaker 3: nerve it must be light, so you see light now.

883
00:47:26,840 --> 00:47:30,520
Speaker 3: Craig who was a psychologist in Cambridge, he wrote he

884
00:47:30,560 --> 00:47:32,600
Speaker 3: wrote this little paper where he said, well, basically, what

885
00:47:32,719 --> 00:47:36,440
Speaker 3: the brain must be doing is constructing this model of

886
00:47:36,640 --> 00:47:40,120
Speaker 3: the outside world of things that it can affect, and

887
00:47:40,239 --> 00:47:44,000
Speaker 3: it's trying to work out what the best things to do,

888
00:47:44,280 --> 00:47:47,719
Speaker 3: how can modulate this outside was so effect Therefore the

889
00:47:47,800 --> 00:47:51,560
Speaker 3: input that it receives in order to achieve whatever ends

890
00:47:51,840 --> 00:47:53,000
Speaker 3: it may be interested in me.

891
00:47:53,080 --> 00:47:53,759
Speaker 4: They're eating you know.

892
00:47:54,000 --> 00:47:58,200
Speaker 3: I'm hungry. I'm right, my body's hungry. Therefore I need

893
00:47:58,280 --> 00:48:00,680
Speaker 3: to find some food, so I've got to looking for it.

894
00:48:00,800 --> 00:48:04,360
Speaker 3: And then the model that the key idea is that

895
00:48:04,440 --> 00:48:07,680
Speaker 3: he's got this suggestion as you say that there's a model,

896
00:48:07,680 --> 00:48:12,000
Speaker 3: a representation of some kind of the outside world, and

897
00:48:12,600 --> 00:48:14,719
Speaker 3: the way you can affect it is both things. It's

898
00:48:14,760 --> 00:48:18,759
Speaker 3: not just a static photo, but it's also a set

899
00:48:18,800 --> 00:48:22,960
Speaker 3: of potential alterations, and you can you can predict, Okay,

900
00:48:23,040 --> 00:48:24,759
Speaker 3: if I go to the shop, I will be able

901
00:48:24,800 --> 00:48:28,040
Speaker 3: to get a cream bun and I will satisfy my hunger.

902
00:48:28,280 --> 00:48:31,440
Speaker 3: If I go to the bookshop, I will not be

903
00:48:31,480 --> 00:48:33,040
Speaker 3: able to buy a cream, but you know, I can

904
00:48:33,080 --> 00:48:35,239
Speaker 3: get a book. But that's not that satisfy a different

905
00:48:35,320 --> 00:48:36,719
Speaker 3: kind of hunger? Is it the same kind of hunger?

906
00:48:36,840 --> 00:48:37,640
Speaker 2: Noe and so on?

907
00:48:38,239 --> 00:48:43,440
Speaker 3: So that model, I mean, is really quite astonishingly powerful,

908
00:48:43,480 --> 00:48:46,920
Speaker 3: the idea that there's this representation at such a complex

909
00:48:47,040 --> 00:48:50,120
Speaker 3: level of the outside world and of our ways of

910
00:48:50,200 --> 00:48:52,080
Speaker 3: altering it that's been developed.

911
00:48:52,120 --> 00:48:54,520
Speaker 4: And I mean, he was working, like many people. He

912
00:48:54,640 --> 00:48:57,080
Speaker 4: was working in the war, and he was working on how.

913
00:48:58,600 --> 00:49:02,800
Speaker 3: People could cope of work in the dark on chips

914
00:49:02,880 --> 00:49:05,240
Speaker 3: and so on, and how they'd be able to predict

915
00:49:05,320 --> 00:49:07,200
Speaker 3: a movement of enemy Aircraft's all.

916
00:49:07,239 --> 00:49:09,800
Speaker 4: This was kind of classic thing that scientists were involved

917
00:49:09,800 --> 00:49:09,920
Speaker 4: in it.

918
00:49:09,960 --> 00:49:12,239
Speaker 3: And in his meantime he was thinking about, well, what

919
00:49:12,600 --> 00:49:15,160
Speaker 3: does it all mean in terms of how the brain

920
00:49:15,280 --> 00:49:18,839
Speaker 3: might function. So this little paper, and there's another one

921
00:49:18,880 --> 00:49:21,640
Speaker 3: as well that he wrote, is astonishing the influential.

922
00:49:21,800 --> 00:49:23,319
Speaker 1: You say in the book that it's going to take

923
00:49:23,400 --> 00:49:26,040
Speaker 1: us a long time to even understand very basic things.

924
00:49:26,160 --> 00:49:29,640
Speaker 1: For example, there are a lot of connectome projects which

925
00:49:29,760 --> 00:49:32,799
Speaker 1: look at, hey, where are all the cells and the connections.

926
00:49:32,840 --> 00:49:35,759
Speaker 1: And let's say a maggot brain, a very tiny little brain.

927
00:49:35,960 --> 00:49:38,480
Speaker 1: You know, what if we understood every single bit of

928
00:49:38,560 --> 00:49:41,360
Speaker 1: the wiring diagram here and yet, as we know, that

929
00:49:41,680 --> 00:49:45,000
Speaker 1: hasn't really unpacked the answer for us.

930
00:49:45,120 --> 00:49:47,240
Speaker 2: And so what do you see coming.

931
00:49:47,080 --> 00:49:47,760
Speaker 1: Down the line?

932
00:49:48,400 --> 00:49:52,719
Speaker 2: How, given this whole enormous, beautiful history that you've written

933
00:49:52,800 --> 00:49:55,320
Speaker 2: of the field of neuroscience, what do you see in

934
00:49:55,400 --> 00:49:56,000
Speaker 2: the future.

935
00:49:56,280 --> 00:50:00,080
Speaker 3: But what we're lacking is is ideas, ways of and

936
00:50:00,120 --> 00:50:01,680
Speaker 3: maybe this is where the llms are going to do

937
00:50:01,719 --> 00:50:03,920
Speaker 3: it without even telling us what the damn ideas are.

938
00:50:04,200 --> 00:50:06,880
Speaker 3: You know, we'll be able to put all the data in,

939
00:50:07,320 --> 00:50:11,760
Speaker 3: put all the connectomic data. I'm sure if eve Marda

940
00:50:11,920 --> 00:50:14,880
Speaker 3: is not doing this, she really should check it in

941
00:50:15,400 --> 00:50:17,520
Speaker 3: see what the AIS makes of it all. I think

942
00:50:17,560 --> 00:50:21,320
Speaker 3: we need more ideas about very concrete things, not about

943
00:50:21,440 --> 00:50:24,759
Speaker 3: how conscious, what consciousness is, or how it emerges. I

944
00:50:24,840 --> 00:50:27,080
Speaker 3: think we need more data on that so we can

945
00:50:27,080 --> 00:50:29,560
Speaker 3: actually agree what we're studying. I guess the other thing

946
00:50:29,640 --> 00:50:34,279
Speaker 3: that maybe you know, it's a negative thing to come

947
00:50:34,360 --> 00:50:36,200
Speaker 3: back to Crick. You know, what he was focused on

948
00:50:36,440 --> 00:50:39,719
Speaker 3: with Christophe Copp for the last twenty years was finding

949
00:50:39,760 --> 00:50:44,280
Speaker 3: the neural correlates of consciousness or of retention of visual awareness.

950
00:50:45,400 --> 00:50:49,680
Speaker 3: And we've done that in that we can actually record

951
00:50:49,800 --> 00:50:54,720
Speaker 3: from you know, people's brains, patients who are very kindly

952
00:50:54,800 --> 00:50:58,359
Speaker 3: given up there allowing people to poke around in their

953
00:50:58,400 --> 00:51:02,319
Speaker 3: brain while they're having an operation, and you can identify

954
00:51:02,440 --> 00:51:04,680
Speaker 3: people have now even been able to reconstruct things that

955
00:51:04,800 --> 00:51:08,280
Speaker 3: they have they have seen using the activity that's recorded.

956
00:51:09,239 --> 00:51:13,280
Speaker 3: So it actually got some of the things that Crick wanted.

957
00:51:13,680 --> 00:51:17,360
Speaker 3: And yet it's still not it's not it. There's something

958
00:51:17,560 --> 00:51:21,560
Speaker 3: still lacking. And maybe I mean the argument of the

959
00:51:21,600 --> 00:51:23,640
Speaker 3: book would be it's going to be the next step

960
00:51:23,680 --> 00:51:26,359
Speaker 3: in technology, and your argument that just being well, yeah,

961
00:51:26,400 --> 00:51:29,520
Speaker 3: we've got that here, Well.

962
00:51:29,480 --> 00:51:31,239
Speaker 4: Let's see maybe that's it, or maybe it's going to

963
00:51:31,280 --> 00:51:32,279
Speaker 4: be something further on.

964
00:51:32,800 --> 00:51:35,759
Speaker 2: Oh, surely it'll be something further on. And by the way.

965
00:51:35,840 --> 00:51:38,960
Speaker 1: One of your arguments is that are metaphors. While they

966
00:51:39,040 --> 00:51:43,360
Speaker 1: can be helpful, they also always constrain absolutely absolutely.

967
00:51:42,960 --> 00:51:45,600
Speaker 3: Can't you know, they're they're I had this cute phrase,

968
00:51:45,680 --> 00:51:48,320
Speaker 3: they're they're they're their frame, their frame. But a frame

969
00:51:48,480 --> 00:51:51,120
Speaker 3: is not just you know, helping you put things on.

970
00:51:51,280 --> 00:51:53,919
Speaker 3: It also limits you can't see outside of the frame.

971
00:51:54,000 --> 00:51:57,120
Speaker 3: And that's that's why things look obvious in the past,

972
00:51:57,160 --> 00:51:59,320
Speaker 3: because we can see the things that they can't. And

973
00:51:59,480 --> 00:52:01,960
Speaker 3: that's why people sometimes think people in the past were stupid.

974
00:52:01,960 --> 00:52:03,800
Speaker 3: But they're not very clever. They're just limited and so

975
00:52:04,120 --> 00:52:07,359
Speaker 3: to we. And this is when scientists, I mean, you've

976
00:52:07,400 --> 00:52:09,160
Speaker 3: been very kind, you've read the book, you've thought about

977
00:52:09,200 --> 00:52:11,040
Speaker 3: a lot. But when you just chat to people and

978
00:52:11,080 --> 00:52:13,719
Speaker 3: they had the unwritten God, that's amazing. And then they said, well,

979
00:52:13,760 --> 00:52:16,319
Speaker 3: what's the next big thing going to be into which

980
00:52:16,360 --> 00:52:18,040
Speaker 3: I say, well, you know, if I knew that, I

981
00:52:18,080 --> 00:52:18,719
Speaker 3: wouldn't tell you.

982
00:52:18,800 --> 00:52:22,120
Speaker 4: I'd be very rich living on my island somewhere. I

983
00:52:22,200 --> 00:52:22,880
Speaker 4: had no idea.

984
00:52:23,120 --> 00:52:26,759
Speaker 3: But it's going to come and it But I mean,

985
00:52:26,840 --> 00:52:29,440
Speaker 3: maybe you're right, I mean I'm maybe I'm not sufficiently

986
00:52:29,520 --> 00:52:32,640
Speaker 3: tuned into the world in particular of kind of human

987
00:52:32,719 --> 00:52:36,799
Speaker 3: euroscience and human brain studies to see that the excitement

988
00:52:36,880 --> 00:52:41,400
Speaker 3: of the parallels with the with the llms maybe changing

989
00:52:41,520 --> 00:52:45,400
Speaker 3: how people are thinking things. But certainly up until twenty twenty,

990
00:52:45,480 --> 00:52:47,719
Speaker 3: which is when the book was finished twenty nineteen, there

991
00:52:47,800 --> 00:52:51,160
Speaker 3: was very much in the tens or whatever we call it,

992
00:52:51,200 --> 00:52:53,360
Speaker 3: the teens, there's very much a sense of stagnation. I

993
00:52:53,400 --> 00:52:55,840
Speaker 3: mean vast amounts of data which were people are drowning in.

994
00:52:56,560 --> 00:52:59,239
Speaker 3: But also and then, what what are we going to

995
00:52:59,280 --> 00:53:02,960
Speaker 3: do now this kind of uncertainty? Maybe that's being resolved.

996
00:53:03,239 --> 00:53:06,400
Speaker 1: Well, I think in your language it would just be

997
00:53:06,719 --> 00:53:09,880
Speaker 1: another frame given to us by the technology things that

998
00:53:10,040 --> 00:53:10,960
Speaker 1: we can't say beyond that.

999
00:53:11,680 --> 00:53:14,160
Speaker 4: But do you think that's happening? Is that a shift

1000
00:53:14,280 --> 00:53:15,840
Speaker 4: in the human.

1001
00:53:17,080 --> 00:53:19,120
Speaker 2: Community for two reasons though.

1002
00:53:19,280 --> 00:53:21,879
Speaker 1: One is looking at llms and saying, wow, that's quite

1003
00:53:21,880 --> 00:53:25,400
Speaker 1: extraordinary what they can produce. The other thing is just

1004
00:53:25,520 --> 00:53:27,800
Speaker 1: being able to use these as a tool to apply

1005
00:53:28,040 --> 00:53:31,040
Speaker 1: them to the massive data sets that we have shored

1006
00:53:31,120 --> 00:53:34,080
Speaker 1: up already and be able to say, oh, here are

1007
00:53:34,120 --> 00:53:37,200
Speaker 1: patterns of the data that we would never have seen ourselves.

1008
00:53:41,719 --> 00:53:45,640
Speaker 1: That was my interview with Matthew Cobb, neuroscientist and author

1009
00:53:45,800 --> 00:53:49,920
Speaker 1: of the idea of the brain. So the central lesson

1010
00:53:50,040 --> 00:53:53,120
Speaker 1: that surfaces in his book is that we draw our

1011
00:53:53,239 --> 00:53:58,279
Speaker 1: metaphors from the technology that exists around us at the time,

1012
00:53:59,000 --> 00:54:02,719
Speaker 1: and as we invent new technologies that gives us new

1013
00:54:02,840 --> 00:54:06,840
Speaker 1: insight into how the brain might be functioning. But also

1014
00:54:07,640 --> 00:54:12,799
Speaker 1: metaphors can limit They are frames that can block out

1015
00:54:12,960 --> 00:54:16,440
Speaker 1: what we're able to see. This shows us something about

1016
00:54:16,680 --> 00:54:19,960
Speaker 1: how science works. Despite everything that we learn in school,

1017
00:54:20,120 --> 00:54:24,120
Speaker 1: science is not just the accumulation of facts, but instead

1018
00:54:24,680 --> 00:54:29,520
Speaker 1: a grasping with our language towards something trying to get

1019
00:54:29,680 --> 00:54:33,760
Speaker 1: more effective metaphors. When we look back at the history

1020
00:54:33,800 --> 00:54:37,200
Speaker 1: of brain science, it's easy to fall into the trap

1021
00:54:37,320 --> 00:54:40,880
Speaker 1: of feeling like the present is the pinnacle that we

1022
00:54:41,000 --> 00:54:44,160
Speaker 1: finally arrived at the right way of thinking about this.

1023
00:54:45,000 --> 00:54:49,360
Speaker 1: We smile at the hydraulic brain of the sixteen hundreds,

1024
00:54:49,480 --> 00:54:53,719
Speaker 1: the clockwork brain of the Enlightenment, the telegraph brain of

1025
00:54:53,760 --> 00:54:58,280
Speaker 1: the eighteen hundreds, the telephone exchange of the late nineteenth century.

1026
00:54:58,719 --> 00:55:03,520
Speaker 1: They seem like charming relics from a simpler time, but

1027
00:55:03,719 --> 00:55:08,200
Speaker 1: each of those metaphors were stepping stones for our thinking.

1028
00:55:08,920 --> 00:55:12,320
Speaker 1: They framed the kind of experiments that people built. The

1029
00:55:12,760 --> 00:55:15,920
Speaker 1: data that they noticed, the questions that they thought it

1030
00:55:16,000 --> 00:55:20,520
Speaker 1: was even possible to ask. Every metaphor illuminated one part

1031
00:55:20,600 --> 00:55:24,520
Speaker 1: of the landscape, even while it left the rest in shadow,

1032
00:55:25,280 --> 00:55:29,279
Speaker 1: and that shadow is still with us. For my whole career,

1033
00:55:29,360 --> 00:55:33,160
Speaker 1: the metaphor of choice was the digital computer, the brain

1034
00:55:33,440 --> 00:55:37,880
Speaker 1: as hardware and software as an information processing device with

1035
00:55:37,960 --> 00:55:41,359
Speaker 1: inputs and outputs and circuits, and that idea has given

1036
00:55:41,440 --> 00:55:46,160
Speaker 1: us extraordinary advances like artificial neural networks and brain machine

1037
00:55:46,200 --> 00:55:52,520
Speaker 1: interfaces and entire fields of computational neuroscience. But it also

1038
00:55:53,160 --> 00:55:56,080
Speaker 1: narrows our vision. And in my book live Wired, I

1039
00:55:56,120 --> 00:55:59,640
Speaker 1: wrote that everything we think about in Silicon Valley is

1040
00:56:00,280 --> 00:56:03,279
Speaker 1: trim and efficient hardware with a layer of software on top.

1041
00:56:03,560 --> 00:56:06,520
Speaker 1: But the brain is clearly so much more than that.

1042
00:56:07,200 --> 00:56:10,759
Speaker 1: That's why I use the term livewear to make this

1043
00:56:10,960 --> 00:56:16,120
Speaker 1: distinction clear. It's a system that is constantly reconfiguring its

1044
00:56:16,160 --> 00:56:20,800
Speaker 1: own circuitry based on experiences. In other words, what flows

1045
00:56:21,040 --> 00:56:24,320
Speaker 1: through the network changes the network. Now we're all watching

1046
00:56:24,880 --> 00:56:29,520
Speaker 1: artificial neural networks like the Transformer architecture, which has changed

1047
00:56:29,560 --> 00:56:33,560
Speaker 1: our world, and those certainly seem to capture something a

1048
00:56:33,640 --> 00:56:37,279
Speaker 1: bit better. But whatever we use and whatever we come

1049
00:56:37,400 --> 00:56:42,319
Speaker 1: up with to make these distinctions. Will our descendants see

1050
00:56:42,719 --> 00:56:49,360
Speaker 1: our metaphors as limiting, just another product of our technological moment.

1051
00:56:49,960 --> 00:56:50,960
Speaker 2: Of course they will.

1052
00:56:51,480 --> 00:56:55,480
Speaker 1: For all we know, there may never be a final metaphor,

1053
00:56:55,640 --> 00:56:58,759
Speaker 1: no single model that captures it all. The brain has

1054
00:56:58,880 --> 00:57:04,800
Speaker 1: its own idio syncratic, massive complexity, and strangeness, making it

1055
00:57:05,360 --> 00:57:09,600
Speaker 1: not quite like a telegraph network, not quite like your laptop,

1056
00:57:10,200 --> 00:57:13,319
Speaker 1: not quite the same as an LLM. We might never

1057
00:57:13,440 --> 00:57:17,080
Speaker 1: have a metaphor that feels complete, because, as they say,

1058
00:57:17,200 --> 00:57:21,080
Speaker 1: the map is never the same as the territory. But

1059
00:57:21,800 --> 00:57:26,800
Speaker 1: the pursuit continues to compel us because every era's metaphor,

1060
00:57:27,000 --> 00:57:32,080
Speaker 1: although it's flawed, still moves us forward. The hydraulic brain

1061
00:57:32,200 --> 00:57:37,200
Speaker 1: led to experiments on fluid pressure. The telegraph brain inspired

1062
00:57:37,240 --> 00:57:41,960
Speaker 1: the search for nerve conduction speeds. The computer brain pushed

1063
00:57:42,040 --> 00:57:45,520
Speaker 1: us into the era of big data and AI. Our

1064
00:57:45,760 --> 00:57:50,240
Speaker 1: past models were all part of the process. So I'm

1065
00:57:50,280 --> 00:57:53,680
Speaker 1: left with two feelings, humility in the face of the

1066
00:57:53,800 --> 00:57:58,280
Speaker 1: unknown and excitement about what's next. Somewhere out there in

1067
00:57:58,400 --> 00:58:02,200
Speaker 1: the midst of the future, there is a technology or

1068
00:58:02,400 --> 00:58:07,000
Speaker 1: an idea that's going to give us a fresh lens

1069
00:58:07,240 --> 00:58:11,160
Speaker 1: one that we can't yet imagine, and with it a

1070
00:58:11,440 --> 00:58:15,520
Speaker 1: new way of asking the oldest question in neuroscience, how

1071
00:58:16,120 --> 00:58:20,160
Speaker 1: does matter give rise to mind? So as you go

1072
00:58:20,240 --> 00:58:24,120
Speaker 1: about your day hearing and seeing and remembering and imagining,

1073
00:58:24,560 --> 00:58:29,000
Speaker 1: consider this, the thing doing all that work is also

1074
00:58:29,160 --> 00:58:34,280
Speaker 1: the thing asking the questions about its own functioning. Despite

1075
00:58:34,480 --> 00:58:37,600
Speaker 1: all that we've learned, it is still, in many ways

1076
00:58:38,160 --> 00:58:41,880
Speaker 1: an undiscovered country, and every day we take a step

1077
00:58:42,040 --> 00:58:51,080
Speaker 1: deeper into that uncharted land. Go to eagleman dot com

1078
00:58:51,200 --> 00:58:54,600
Speaker 1: slash podcast for more information and to find further reading.

1079
00:58:55,200 --> 00:58:58,360
Speaker 1: Join the weekly discussions on my substack, and check out

1080
00:58:58,400 --> 00:58:59,440
Speaker 1: and subscribe to Inner.

1081
00:58:59,360 --> 00:59:01,520
Speaker 2: Cosmos on YouTube for videos.

1082
00:59:01,200 --> 00:59:05,160
Speaker 1: Of each episode and to leave comments until next time.

1083
00:59:05,440 --> 00:59:08,240
Speaker 1: I'm David Eagleman, and this is Inner Cosmos.